Research on Scientific Derivation of Control Limits in Control Charts

Control Charts (CC) are the means to “manage the process behaviour” by analysing subsequent samples at regular intervals of time.: good decisions depend on Scientific analysis of data. Often, the data are considered Normally distributed; this is not completely right; data must be analysed according to their distribution: decisions are different with different distributions, because the Control Limits of the CC depend on the distribution. We compare our findings with Shewhart findings; later we extend the ideas to deal with “rare events”, with data not Normally distributed; we compare our results, found by RIT, for various cases in the literature: there is a big difference between the Shewhart CC and the Time Between Events CC; considering that, future decisions of Decision Makers will be both sounder and cheaper, when data are not normally distributed. ARL depends on the data distribution, not only on the “false alarm rate”. The novelty of the paper is due to the Scientific Way of Computing the Control Limits, both for the mean and for the variance

Constructing a Control Chart Using Functional Data

[Abstract] This study proposes a control chart based on functional data to detect anomalies and estimate the normal output of industrial processes and services such as those related to the energy efficiency domain. Companies providing statistical consultancy services in the fields of energy efficiency; heating, ventilation and air conditioning (HVAC); installation and control; and big data for buildings, have been striving to solve the problem of automatic anomaly detection in buildings controlled by sensors. Given the functional nature of the critical to quality (CTQ) variables, this study proposed a new functional data analysis (FDA) control chart method based on the concept of data depth. Specifically, it developed a control methodology, including the Phase I and II control charts. It is based on the calculation of the depth of functional data, the identification of outliers by smooth bootstrap resampling and the customization of nonparametric rank control charts. A comprehensive simulation study, comprising scenarios defined with different degrees of dependence between curves, was conducted to evaluate the control procedure. The proposed statistical process control procedure was also applied to detect energy efficiency anomalies in the stores of a textile company in the Panama City. In this case, energy consumption has been defined as the CTQ variable of the HVAC system. Briefly, the proposed methodology, which combines FDA and multivariate techniques, adapts the concept of the control chart based on a specific case of functional data and thereby presents a novel alternative for controlling facilities in which the data are obtained by continuous monitoring, as is the case with a great deal of process in the framework of Industry 4.0.This study has been funded by the eCOAR project (PC18/03) of CITIC. The work of Salvador Naya, Javier Tarrío-Saavedra, Miguel Flores and Rubén Fernández-Casal has been supported by MINECO grants MTM2014-52876-R, MTM2017-82724-R, the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2016-015, and Centro Singular de Investigación de Galicia ED431G/01 2016-19), through the ERDF. The research of Miguel Flores has been partially supported by Grant PII-DM-002-2016 of Escuela Politécnica Nacional of EcuadorXunta de Galicia; ED431C-2016-015Xunta de Galicia; ED431G/01 2016-19Escuela Politécnica Nacional de Ecuador; PII-DM-002-201

Quality Decisions Based on Time between Events Data Analysis

Good decisions (Quality Decisions) depend on scientific analysis of data. Data are collected, generally, in two ways: 1) one sample of suitable size, 2) subsequent samples, at regular intervals of time. Often the data are considered normally distributed. This is wrong because the data must be analysed according to their distribution: Decisions are different. In several cases the data are exponentially distributed: we see how to scientifically deal with Control Charts (CC) to decide; this is opposite to what gives the T Charts that are claimed to be a good method for dealing with “rare events”: The Minitab Software (19 & 20 & 21) for “T Charts” is considered. The author will compare some methods, found in the literature with the author’s Theory RIT (Reliability Integral Theory): We will see various cases found in the literature. Classical Shewhart Control Charts and the TBE (Time Between Events) Control Charts have been considered: it appears that with RIT the future decisions will be both sounder and cheaper, for data is exponentially distributed. The novelty of the paper is in the scientific way of dealing with the Control Charts and their Control Limits, both with normally distributed data and with exponentially distributed data. In this way, a lot of wrong published papers on “Time Between Events” are to be discarded, even if their authors claim “We used Standard Statistical methods, typical in the vast literature of similar papers”. The author had to self-cite because it seems the only one that has been fighting for years for “Papers Quality”; he humbly asked the readers to inform him if some people did the sam

Forecasting sovereign bonds markets using machine learning: forecasting the portuguese government bond using machine learning approach

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementFinancial markets, due to their non-linear, volatile and complex nature turn any type of forecasting into a difficult task, as the classical statistical methods are no longer adequate. Many factors exist that can influence the government bonds yields and how these bonds behave. The consequence of the behaviour of these bonds are extended over geographies and individuals. As the financial markets grow bigger, more investors are trying to develop systematic approaches that are intended to predict prices and movements. Machine Learning algorithms already proven their value in predicting and finding patterns in many subjects. When it comes to financial markets, Machine Learning is not a new tool. It is already widely used to predict behaviours and trends with some degree of success. This dissertation aims to study the application of two Machine Learning algorithms - Genetic Programming (GP) and Long Short-Term Memory (LSTM) - to the Portuguese Government 10Y Bond and try to forecast the yield with accuracy. The construction of the predictive models is based on historical information of the bond and on other important factors that influence its behaviour, extracted through the Bloomberg Portal. In order to analyse the quality of the two models, the results of each algorithm will be compared. An analysis will be presented regarding the quality of the results from both algorithms and the respective time cost. In the end, each model will be discussed and conclusions will be taken about which one can be the answer to the main question of this study, which is “What will the Yield of the Portuguese Government 10Y Bond be on T+1?”. The results obtained showed that Genetic Programming can create a model with higher accuracy. However, Long Short-Term Memory should not be ignored because it can also point to good results. Regarding execution time, velocity is a problem when it comes to Genetic Programming. This algorithm takes more time to execute compared to LSTM. Long Short-Term Memory is considerably quicker to get results. In order to take the right decision about which model to choose one must keep in mind the priorities. In case accuracy is the priority, Genetic Programming will be the answer. Nevertheless, when velocity is the priority Long Short-Term Memory should be the choice

Analysis of life cycle management leading to pharmaceutical process improvement by computer simulation

The pharmaceutical industry participates in a highly changing environment with increasing demands and competition while being less innovative. The development of medicinal products and their value towards ordinary goods force the manufacturers to produce high quality products in the most cost-effective way. By analyzing the life cycle of medicinal products and its management, the present challenges as well as appropriate solutions were identified. One such solution is computer simulation, which is why two approved production processes of film-coated tablets were optimized by discrete- event simulations. Through this, a methodological approach was developed to build, verify, and validate models of the as-is productions. Afterwards, the models were modified into different optimization scenarios to challenge multiple shift systems. These shift systems were evaluated considering the campaign duration, the production costs as well as the capacity utilizations of employees and machines. The implemented model changes could bisect the campaign duration and reduce the production costs in a two-digit percentage share. Thus, process optimizations by computer simulations were proved to be one remarkable strategy in the life cycle management of medicinal products.Die pharmazeutische Industrie partizipiert in einer sich stark verändernden Umgebung mit steigendenen Anforderungen sowie wachsender Konkurrenz und ist zugleich selbst weniger innovativ. Die Entwicklung von Arzneimitteln und deren Wert hin zu normalen Gütern zwingt die Hersteller möglichst kosteneffizient qualitativ hochwertige Produkte zu fertigen. Eine Analyse des Lebenszyklus von Arzneimitteln und dessen Management identifizierte sowohl die vorhandenen Herausforderungen als auch mögliche Lösungsansätze. Computer Simulationen stellen einen solchen Lösungsansatz dar, sodass zwei zugelassene Produktionsprozesse von Filmtabletten durch Simulationen optimiert wurden. Dafür wurde zuerst ein methodisches Vorgehen entwickelt um Modelle der Produktionsprozesse zu erstellen, sie zu verifizieren und zu validieren. Im Anschluss wurden diese Modelle in verschiedene Optimierungsszenarien abgewandelt um unterschiedliche Schichtsysteme zu prüfen. Deren Bewertung erfolgte anhand von Kampagnendauer, Produktionskosten sowie Mitarbeiter- und Maschinenauslastungen. Die implementierten Modelländerungen konnten die Dauer der Produktionskampagnen halbieren und die Produktionskosten um einen zweistelligen Prozentsatz senken. Somit wurde bewiesen, dass Prozessoptimierungen durch Computer Simulationen eine eindrucksvolle Strategie im Life Cycle Management von Arzenimitteln darstellen

Multivariate Birnbaum-Saunders Distributions: Modelling and Applications

Since its origins and numerous applications in material science, the Birnbaum–Saunders family of distributions has now found widespread uses in some areas of the applied sciences such as agriculture, environment and medicine, as well as in quality control, among others. It is able to model varied data behaviour and hence provides a flexible alternative to the most usual distributions. The family includes Birnbaum–Saunders and log-Birnbaum–Saunders distributions in univariate and multivariate versions. There are now well-developed methods for estimation and diagnostics that allow in-depth analyses. This paper gives a detailed review of existing methods and of relevant literature, introducing properties and theoretical results in a systematic way. To emphasise the range of suitable applications, full analyses are included of examples based on regression and diagnostics in material science, spatial data modelling in agricultural engineering and control charts for environmental monitoring. However, potential future uses in new areas such as business, economics, finance and insurance are also discussed. This work is presented to provide a full tool-kit of novel statistical models and methods to encourage other researchers to implement them in these new areas. It is expected that the methods will have the same positive impact in the new areas as they have had elsewhere